
from pandas import read_csv
import pandas as pd
from pandas.plotting import lag_plot
import seaborn as sn
import matplotlib.pyplot as plt
import numpy as np
import os
import zipfile
import io
from sklearn.preprocessing import MinMaxScaler
import matplotlib.cm as cm


lectura = read_csv('../Datos_originales/casos_diarios_total_regional.csv', skiprows = 4, encoding='latin-1', sep = ';', na_values = ['NaN', 'NaT'])

# Headers = columnas de mi conjunto de datos. Si no especificamos asume que son las de la primera fila leida
# Skiprows = n: con n = numero de filas que quiero saltar. Las cuatro primeras son texto -> las saltamos
# Encoding = 'latin-1': para que no de errores de codificacion de algunos caracteres 
# Sep = ';': delimitador que separa las columnas
# Index_col = 0: usar la primera columna (fechas) como filas del dataframe


datos = lectura.rename(columns={'Pruebas_PCR_Ag':'Pruebas',
                                'Casos_incidentes_PCR/Ag+':'Incidentes',
                                'Hospitalizados': 'H',
                                'Hospitaliz_UCI':'H_UCI',
                                'Asintomatico_hombre_<45años': 'Asint_H_<45',
                                'Asintomatico_hombre_45a64años': 'Asint_H_45a64',
                                'Asintomatico_hombre_+65años': 'Asint_H_+65',
                                'Sintomatico_hombre_<45años': 'Sint_H_<45',
                                'Sintomatico_hombre_45a64años': 'Sint_H_45a64',
                                'Sintomatico_hombre_+65años': 'Sint_H_+65',
                                'Asintomatico_mujer_<45años': 'Asint_M_<45',
                                'Asintomatico_mujer_45a64años': 'Asint_M_45a64',
                                'Asintomatico_mujer_+65años': 'Asint_M_+65',
                                'Sintomatico_mujer_<45años': 'Sint_M_<45',
                                'Sintomatico_mujer_45a64años': 'Sint_M_45a64',
                                'Sintomatico_mujer_+65años': 'Sint_M_+65'})


df_salud_murcia = pd.DataFrame(datos)
df_salud_murcia.head(3)


format = '%d/%m/%Y'
datetime_fecha = pd.to_datetime(df_salud_murcia['Fecha'], format = format)
df_salud_murcia = df_salud_murcia.drop(['Fecha'], axis=1)
df_salud_murcia.insert(0,'Fecha', datetime_fecha)
df_salud_murcia = df_salud_murcia.set_index('Fecha')
df_salud_murcia.head(3)


df_salud_murcia.dropna(axis = 0, inplace = True)


df_salud_murcia['Asintomaticos'] = df_salud_murcia['Asint_H_<45'] + df_salud_murcia['Asint_H_45a64'] + df_salud_murcia['Asint_H_+65'] + df_salud_murcia['Asint_M_<45'] + df_salud_murcia['Asint_M_45a64'] + df_salud_murcia['Asint_M_+65']
df_salud_murcia['Sintomaticos'] = df_salud_murcia['Sint_H_<45'] + df_salud_murcia['Sint_H_45a64'] + df_salud_murcia['Sint_H_+65'] + df_salud_murcia['Sint_M_<45'] + df_salud_murcia['Sint_M_45a64'] + df_salud_murcia['Sint_M_+65']


df_salud_murcia = df_salud_murcia.drop(['Asint_H_<45', 'Asint_H_45a64', 'Asint_H_+65', 'Asint_M_<45', 'Asint_M_45a64', 'Asint_M_+65', 'Sint_H_<45', 'Sint_H_45a64', 'Sint_H_+65', 'Sint_M_<45', 'Sint_M_45a64', 'Sint_M_+65'], axis=1)


df_salud_murcia.to_csv(path_or_buf = '../Datos_preprocesados/salud_murcia.csv')


def conversor_a_dataframe(lista_fechas, lista_entradas, lista_salidas):
    format = '%Y-%d-%m'
    # Convertimos en DatatimeIndex
    fechas_serie = pd.to_datetime(lista_fechas, format = format)
    # Creamos dataframe con las entradas salidas y fechas
    df_entradas_salidas = pd.DataFrame(list(zip(lista_entradas, lista_salidas)), columns = ['Entradas', 'Salidas'], index = fechas_serie).rename_axis('Fecha', axis = 0)
    # Definimos las series para ordenar
    df_entradas_salidas['month'] = fechas_serie.month
    df_entradas_salidas['year'] = fechas_serie.year
    df_entradas_salidas['day'] = fechas_serie.day
    # Ordenamos por mes-dia-año
    df_entradas_salidas = df_entradas_salidas.sort_values(['year', 'month', 'day']).drop(columns=['day', 'month', 'year'])
    return df_entradas_salidas


def parseador_fechas_mitma(nombre):
    # Cogemos el día
    dia = nombre[6:8]
    mes = nombre[4:6]
    anio = nombre[0:4]
    return(anio+'-'+dia+'-'+mes)


# Los diferentes al resto
codigos = ['30901', '30902']

for i in range(1,44):
    if i < 10 and i not in [4,6]:
        codigos.append('3000'+str(i))
    if i >= 10 and i not in [14,27,29,31,32,34,39,40,42]:
        codigos.append('300'+str(i))
    if i == 4:
        codigos.append('30014_AM')
    if i == 6:
        codigos.append('30039_AM')
    if i == 14:
        codigos.append('30014_AM')
    if i == 27:
        codigos.append('3002701')
    if i == 29:
        codigos.append('30029_AM')
    if i == 31:
        codigos.append('30042_AM')
    if i == 32:
        codigos.append('30029_AM')
    if i == 34:
        codigos.append('30014_AM')
    if i == 39:
        codigos.append('30039_AM')
    if i == 40:
        codigos.append('30042_AM')
    if i == 42:
        codigos.append('30042_AM')


ruta = '../Datos_originales/mitma_movilidad/'

lista_fechas = []
lista_entradas = []
lista_salidas = []


# Cogemos los archivos .tar mensuales
archivos = os.listdir(ruta)

for a in archivos:
    meses = os.listdir(ruta+a)
    for m in meses:
        if "._" not in m:
            # Obtenemos la fecha
            fecha = parseador_fechas_mitma(m)
            lista_fechas.append(fecha)
            # Leemos el archivo correspondiente
            df = pd.read_csv(ruta+a+'/'+m, compression='gzip', sep='|', header=0, low_memory = False)
            # Nos quedamos solo con los viajes en la Región
            df_s = df[df['origen'].isin(codigos)]
            # Eliminamos los viajes dentro de la Región
            df_s = df_s[~df_s['destino'].isin(codigos)]
            df_e = df[df['destino'].isin(codigos)]
            # Eliminamos los viajes de Murcia a Murcia
            df_e = df_e[~df_e['origen'].isin(codigos)]
            # Sumamos todos los viajes que son las entradas y salidas
            salidas = df_s['viajes'].sum()
            entradas = df_e['viajes'].sum()
            # Añadimos a las listas
            lista_salidas.append(salidas)
            lista_entradas.append(entradas)


df_mitma = conversor_a_dataframe(lista_fechas, lista_entradas, lista_salidas)
df_mitma.tail(3)


df_mitma.shape

(374, 2)


df_mitma.isna().sum()

Entradas    0
Salidas     0
dtype: int64


df_mitma.to_csv(path_or_buf = '../Datos_preprocesados/movilidad.csv')


df_salud_movilidad = pd.concat([df_salud_murcia, df_mitma], axis=1)


df_salud_movilidad.head(3)


df_salud_movilidad.dropna(axis = 0, inplace = True)


df_salud_movilidad.to_csv(path_or_buf = '../Datos_preprocesados/salud_movilidad.csv')


df_salud_movilidad = read_csv('../Datos_preprocesados/salud_movilidad.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
df_salud_movilidad = df_salud_movilidad.set_index('Fecha')


df_salud_movilidad.head(3)


df_salud_movilidad.shape

(358, 9)


df_salud_movilidad.isna().sum()

Pruebas          0
Incidentes       0
H                0
H_UCI            0
Defunciones      0
Asintomaticos    0
Sintomaticos     0
Entradas         0
Salidas          0
dtype: int64


num = 0

# Initialize the figure style
plt.style.use('seaborn-white')
 
# create a color palette

palette = plt.get_cmap('prism')

for column in df_salud_movilidad:
    
    num = num + 1
 
    # Find the right spot on the plot
    plt.subplot(7,3, num)
 
    if "Salidas" in column or "Entradas" in column:
        serie = df_salud_movilidad[column]
        serie.plot(style = '.', color = palette(num), linewidth = 0.8, alpha = 0.9, label = column)
    else:
       # Plot the lineplot
        plt.plot(df_salud_movilidad[column], marker = '', color = palette(num), linewidth = 0.8, alpha = 0.9, label = column)

    # Add title
    plt.title(column, loc ='center', fontsize = 12, fontweight = 0, color = palette(num))

    plt.xticks([])
    #plt.xticks(rotation=90)
    
plt.subplots_adjust(left = 0.01, right = 4, top = 5, bottom = 0.01, hspace = 0.2, wspace = 0.2)


# Show the graph
plt.show()


def crear_predictor(lag):
    df_predictor = df_salud_movilidad.copy()
    columnas = df_predictor.columns
    lista = []
    for c in columnas:
        datos = pd.DataFrame(df_predictor[c].values, index = df_predictor.index)
        for i in range(1,lag+1):
            df_predictor.insert(len(df_predictor.columns), c+' t-'+str(i),datos.shift(i))
            lista.append(df_predictor.columns)

    columnas = columnas.drop("Incidentes")
    df_predictor = df_predictor.drop(columnas, axis=1)
    return df_predictor


df_predictor_lag3 = crear_predictor(3)
df_predictor_lag3.dropna(axis = 0, inplace = True)
df_predictor_lag3.head(3)


df_predictor_lag3.to_csv(path_or_buf = '../Datos_preprocesados/predictor_lag3.csv')


df_predictor_lag3 = read_csv('../Datos_preprocesados/predictor_lag3.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
df_predictor_lag3 = df_predictor_lag3.set_index('Fecha')
df_predictor_lag3.head(3)


df_predictor_lag5 = crear_predictor(5)
df_predictor_lag5.dropna(axis = 0, inplace = True)
df_predictor_lag5.head(3)


df_predictor_lag5.to_csv(path_or_buf = '../Datos_preprocesados/predictor_lag5.csv')


df_predictor_lag5 = read_csv('../Datos_preprocesados/predictor_lag5.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
df_predictor_lag5 = df_predictor_lag5.set_index('Fecha')
df_predictor_lag5.head(3)


def escalar_datos(df):
    index = df.index
    scaler = MinMaxScaler()
    escalado = scaler.fit_transform(df)
    df_escalado = pd.DataFrame(escalado, columns = df.columns).set_index(index)
    return df_escalado


df_predictor_lag3_escalado = escalar_datos(df_predictor_lag3)
df_predictor_lag3_escalado.head(3)


df_predictor_lag3_escalado.to_csv(path_or_buf = '../Datos_preprocesados/predictor_lag3_escalado.csv')


df_predictor_lag3_escalado = read_csv('../Datos_preprocesados/predictor_lag3_escalado.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
df_predictor_lag3_escalado = df_predictor_lag3_escalado.set_index('Fecha')
df_predictor_lag3_escalado.head(3)


df_predictor_lag5_escalado = escalar_datos(df_predictor_lag5)
df_predictor_lag5_escalado.head(3)


df_predictor_lag5_escalado.to_csv(path_or_buf = '../Datos_preprocesados/predictor_lag5_escalado.csv')


df_predictor_lag5_escalado = read_csv('../Datos_preprocesados/predictor_lag5_escalado.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
df_predictor_lag5_escalado = df_predictor_lag5_escalado.set_index('Fecha')
df_predictor_lag5_escalado.head(3)


# Imports
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RepeatedKFold
from sklearn.model_selection import train_test_split
from sklearn.model_selection import TimeSeriesSplit
import sklearn.metrics as sm
from sklearn.linear_model import Lasso
from sklearn.linear_model import ElasticNet
from sklearn import svm
from sklearn.linear_model import LinearRegression 
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor
import math
# Para guardar los modelos entrenados
import joblib as joblib
# Para crossvalidación
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import cross_validate


cv = TimeSeriesSplit(n_splits = 10)


def search_grid(estimador, grid, X_train, y_train):
    # Buscamos los mejores hiperparametros
    clf = GridSearchCV(estimator=estimador, 
                        param_grid=grid,
                        cv=cv,
                        #refit=True,
                        #error_score=0,
                        # POR DEFECTO -> R^2
                        #scoring = miscorer,#'neg_root_mean_squared_error',
                        scoring = 'neg_root_mean_squared_error',
                        #scoring = miscorer,
                        n_jobs=-1,
                        return_train_score=True)

    clf.fit(X_train, y_train)
    
    return clf


def MAE(y_pred, y_test):
    mae = round(sm.mean_absolute_error(y_test, y_pred), 2)
    print("Mean absolute error =", mae)
    return mae


def RMSE(y_pred, y_test):
    rmse = round(math.sqrt(sm.mean_squared_error(y_test, y_pred)), 2)
    print("RMSE =", rmse)
    return rmse


def R2(y_pred, y_test):
    r2 = round(sm.r2_score(y_test, y_pred), 2)
    print("R2 =", r2)
    return r2


def grafica(p, t, i):
    
    #plt.subplot(3,3,1)
    pd.Series(p).plot(color = 'darkorange', linewidth = 1.4, alpha = 0.9, label = 'Incidentes predecidos')
    t.plot(color = 'deepskyblue', linewidth = 1.4, alpha = 0.9, label = 'Incidentes Reales') 
    
    plt.title('Indicentes t+'+str(i+1), loc ='center', fontsize = 12, fontweight = 0, color = 'black')
    
    plt.legend(loc='best')

    plt.xticks(rotation=90)
    plt.show()


def calcular_predicciones_lag3(modelo, y_pred, X_test):
    predicciones = []
    X_test_modelo = X_test.copy()
    for i in range(1,8):
        # Modificamos el conjunto de test
        X_test_modelo['Incidentes t-3'] = X_test_modelo['Incidentes t-2']
        X_test_modelo['Incidentes t-2'] = X_test_modelo['Incidentes t-1']
        X_test_modelo['Incidentes t-1'] = y_pred
        # Predecimos
        y_pred = modelo.predict(X_test_modelo)
        # Ajustamos al tamaño
        pred = y_pred[i:]
        predicciones.append(pred)
    return predicciones


def calcular_predicciones_lag5(modelo, y_pred, X_test):
    predicciones = []
    X_test_modelo = X_test.copy()
    for i in range(1,8):
        # Modificamos el conjunto de test
        X_test_modelo['Incidentes t-5'] = X_test_modelo['Incidentes t-4']
        X_test_modelo['Incidentes t-4'] = X_test_modelo['Incidentes t-3']
        X_test_modelo['Incidentes t-3'] = X_test_modelo['Incidentes t-2']
        X_test_modelo['Incidentes t-2'] = X_test_modelo['Incidentes t-1']
        X_test_modelo['Incidentes t-1'] = y_pred
        # Predecimos
        y_pred = modelo.predict(X_test_modelo)
        # Ajustamos al tamaño
        pred = y_pred[i:]
        predicciones.append(pred)
    return predicciones


def graficas_errores(predicciones, y_test):
    erroresMAE = []
    erroresRMSE = []
    erroresR2 = []
    test = y_test.copy()
    for i in range(1,len(predicciones)+1):
        # Tomo la predicción en t+i
        s = pd.Series(predicciones[i-1])
        # Elimino el día que ya no uso
        test = test.drop([test.index[0]])
        grafica(s,test,i-1)
        # Añado los errores a la lista de errores
        erroresMAE.append(MAE(predicciones[i-1],test))
        erroresRMSE.append(RMSE(predicciones[i-1],test))
        erroresR2.append(R2(predicciones[i-1],test))
    return erroresMAE, erroresRMSE, erroresR2


def visualizar_predicciones(y_test, predicciones):
    
    # TODO : CREO NO NECESARIO
    test = y_test.copy()

    palette = plt.get_cmap('prism')

    for i in range(1,len(predicciones)+1):
        # Tomo la predicción en t+i
        s = pd.Series(predicciones[i-1])
        # Para mostrarla, la desplazo i valores a la derecha para colocarla en el dia correspondiente de prediccion
        s = s.shift(+i)
        s.dropna(axis = 0, inplace = True)

        s.plot(color = palette(i), linewidth = 1.4, alpha = 0.9, label = 'Incidentes predecidos t+'+str(i))

        # TODO : CREO NO NECESARIO
        test = test.drop([test.index[0]])

    y_test.plot(color = 'deepskyblue', linewidth = 1.4, alpha = 0.9, label = 'Incidentes Reales') 

    plt.title('Evolución incidentes', loc ='center', fontsize = 12, fontweight = 0, color = 'black')

    plt.legend(loc='best')

    plt.xticks(rotation=90)
    plt.show()


X_train_3, X_test_3, y_train_3, y_test_3 = train_test_split(df_predictor_lag3_escalado.drop(['Incidentes'], axis=1),
                                        df_predictor_lag3_escalado['Incidentes'], train_size = 0.8, test_size = 0.2, 
                                        random_state = 42, shuffle = False)


X_train_5, X_test_5, y_train_5, y_test_5 = train_test_split(df_predictor_lag5_escalado.drop(['Incidentes'], axis=1),
                                        df_predictor_lag5_escalado['Incidentes'], train_size = 0.8, test_size = 0.2, 
                                        random_state = 42, shuffle = False)


rf = RandomForestRegressor(random_state = 42)


rf.get_params()


grid = {
    'bootstrap': [True],
    'max_depth': [20, 50, 60, 70, 80],
    'max_features': [2, 3, 5],
    'min_samples_leaf': [1, 2, 4, 5],
    'min_samples_split': [4, 8, 10, 14],
    'n_estimators': [100, 300, 500, 1000, 1500]
}


clf_rf = search_grid(rf, grid, X_train_3, y_train_3)


optimised_rf = clf_rf.best_estimator_


clf_rf.best_params_

{'bootstrap': True,
 'max_depth': 20,
 'max_features': 5,
 'min_samples_leaf': 1,
 'min_samples_split': 10,
 'n_estimators': 300}


rf_results = pd.DataFrame(clf_rf.cv_results_)
rf_results.head(3)


joblib.dump(optimised_rf, 'rf_lag3_escalado.pkl')

['rf_lag3_escalado.pkl']


optimised_rf_lag3 = joblib.load('rf_lag3_escalado.pkl')


y_pred = optimised_rf_lag3.predict(X_test_3)


RMSEs_test_lag3_rf = []
MAEs_test_lag3_rf = []
R2s_test_lag3_rf = []

RMSEs_test_lag3_rf.append(RMSE(y_pred, y_test_3))
MAEs_test_lag3_rf.append(MAE(y_pred, y_test_3))
R2s_test_lag3_rf.append(R2(y_pred, y_test_3))

RMSE = 0.22
Mean absolute error = 0.14
R2 = 0.4


grafica(y_pred, y_test_3, -1)


np.corrcoef(y_test_3, y_pred)

array([[1.        , 0.84651874],
       [0.84651874, 1.        ]])


predicciones_rf_lag3 = calcular_predicciones_lag3(optimised_rf_lag3, y_pred, X_test_3)


visualizar_predicciones(y_test_3, predicciones_rf_lag3)


MAE_rf, RMSE_rf, R2_rf = graficas_errores(predicciones_rf_lag3, y_test_3)

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.37

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.36

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.36

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.36

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.35

Mean absolute error = 0.15
RMSE = 0.24
R2 = 0.34

Mean absolute error = 0.16
RMSE = 0.24
R2 = 0.34


for i in range(0, len(RMSE_rf)):
    RMSEs_test_lag3_rf.append(RMSE_rf[i])
    MAEs_test_lag3_rf.append(MAE_rf[i])
    R2s_test_lag3_rf.append(R2_rf[i])


clf_rf = search_grid(rf, grid, X_train_5, y_train_5)


optimised_rf = clf_rf.best_estimator_


clf_rf.best_params_

{'bootstrap': True,
 'max_depth': 20,
 'max_features': 5,
 'min_samples_leaf': 2,
 'min_samples_split': 8,
 'n_estimators': 100}


rf_results = pd.DataFrame(clf_rf.cv_results_)
rf_results.head(3)


joblib.dump(optimised_rf, 'rf_lag5_escalado.pkl')

['rf_lag5_escalado.pkl']


optimised_rf_lag5 = joblib.load('rf_lag5_escalado.pkl')


y_pred = optimised_rf_lag5.predict(X_test_5)


RMSEs_test_lag5_rf = []
MAEs_test_lag5_rf = []
R2s_test_lag5_rf = []

RMSEs_test_lag5_rf.append(RMSE(y_pred, y_test_5))
MAEs_test_lag5_rf.append(MAE(y_pred, y_test_5))
R2s_test_lag5_rf.append(R2(y_pred, y_test_5))

RMSE = 0.23
Mean absolute error = 0.14
R2 = 0.37


grafica(y_pred, y_test_5, -1)


np.corrcoef(y_test_5, y_pred)

array([[1.        , 0.85867788],
       [0.85867788, 1.        ]])


predicciones_rf_lag5 = calcular_predicciones_lag5(optimised_rf_lag5, y_pred, X_test_5)


visualizar_predicciones(y_test_5, predicciones_rf_lag5)


MAE_rf, RMSE_rf, R2_rf = graficas_errores(predicciones_rf_lag5, y_test_5)

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.35

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.35

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.35

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.35

Mean absolute error = 0.15
RMSE = 0.24
R2 = 0.34

Mean absolute error = 0.16
RMSE = 0.24
R2 = 0.33

Mean absolute error = 0.16
RMSE = 0.24
R2 = 0.33


for i in range(0, len(RMSE_rf)):
    RMSEs_test_lag5_rf.append(RMSE_rf[i])
    MAEs_test_lag5_rf.append(MAE_rf[i])
    R2s_test_lag5_rf.append(R2_rf[i])


regresion_lineal = LinearRegression()


regresion_lineal.fit(X_train_3, y_train_3)

LinearRegression()


joblib.dump(regresion_lineal, 'regresion_lineal_lag3_escalado.pkl')

['regresion_lineal_lag3_escalado.pkl']


regresion_lineal_lag3 = joblib.load('regresion_lineal_lag3_escalado.pkl')


y_pred = regresion_lineal_lag3.predict(X_test_3)


RMSEs_test_lag3_LR = []
MAEs_test_lag3_LR = []
R2s_test_lag3_LR = []

RMSEs_test_lag3_LR.append(RMSE(y_pred, y_test_3))
MAEs_test_lag3_LR.append(MAE(y_pred, y_test_3))
R2s_test_lag3_LR.append(R2(y_pred, y_test_3))

RMSE = 0.13
Mean absolute error = 0.09
R2 = 0.78


grafica(y_pred, y_test_3, -1)


np.corrcoef(y_test_3, y_pred)

array([[1.        , 0.90277771],
       [0.90277771, 1.        ]])


predicciones_LR_lag3 = calcular_predicciones_lag3(regresion_lineal_lag3, y_pred, X_test_3)


visualizar_predicciones(y_test_3, predicciones_LR_lag3)


MAE_LR, RMSE_LR, R2_LR = graficas_errores(predicciones_LR_lag3, y_test_3)

Mean absolute error = 0.11
RMSE = 0.18
R2 = 0.61

Mean absolute error = 0.14
RMSE = 0.21
R2 = 0.46

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.39

Mean absolute error = 0.17
RMSE = 0.27
R2 = 0.13

Mean absolute error = 0.19
RMSE = 0.3
R2 = -0.03

Mean absolute error = 0.21
RMSE = 0.32
R2 = -0.17

Mean absolute error = 0.23
RMSE = 0.35
R2 = -0.41


for i in range(0, len(RMSE_LR)):
    RMSEs_test_lag3_LR.append(RMSE_LR[i])
    MAEs_test_lag3_LR.append(MAE_LR[i])
    R2s_test_lag3_LR.append(R2_LR[i])


regresion_lineal.fit(X_train_5, y_train_5)

LinearRegression()


joblib.dump(regresion_lineal, 'regresion_lineal_lag5_escalado.pkl')

['regresion_lineal_lag5_escalado.pkl']


regresion_lineal_lag5 = joblib.load('regresion_lineal_lag5_escalado.pkl')


y_pred = regresion_lineal_lag5.predict(X_test_5)


RMSEs_test_lag5_LR = []
MAEs_test_lag5_LR = []
R2s_test_lag5_LR = []

RMSEs_test_lag5_LR.append(RMSE(y_pred, y_test_5))
MAEs_test_lag5_LR.append(MAE(y_pred, y_test_5))
R2s_test_lag5_LR.append(R2(y_pred, y_test_5))

RMSE = 0.13
Mean absolute error = 0.08
R2 = 0.8


grafica(y_pred, y_test_5, -1)


predicciones_LR_lag5 = calcular_predicciones_lag5(regresion_lineal_lag5, y_pred, X_test_5)


visualizar_predicciones(y_test_5, predicciones_LR_lag5)


MAE_LR, RMSE_LR, R2_LR = graficas_errores(predicciones_LR_lag5, y_test_5)

Mean absolute error = 0.11
RMSE = 0.16
R2 = 0.69

Mean absolute error = 0.12
RMSE = 0.17
R2 = 0.65

Mean absolute error = 0.13
RMSE = 0.19
R2 = 0.56

Mean absolute error = 0.17
RMSE = 0.25
R2 = 0.28

Mean absolute error = 0.17
RMSE = 0.25
R2 = 0.24

Mean absolute error = 0.21
RMSE = 0.3
R2 = -0.09

Mean absolute error = 0.23
RMSE = 0.33
R2 = -0.28


for i in range(0, len(RMSE_LR)):
    RMSEs_test_lag5_LR.append(RMSE_LR[i])
    MAEs_test_lag5_LR.append(MAE_LR[i])
    R2s_test_lag5_LR.append(R2_LR[i])


svr = svm.SVR()


svr.get_params()

{'C': 1.0,
 'cache_size': 200,
 'coef0': 0.0,
 'degree': 3,
 'epsilon': 0.1,
 'gamma': 'scale',
 'kernel': 'rbf',
 'max_iter': -1,
 'shrinking': True,
 'tol': 0.001,
 'verbose': False}


grid = {'C' : [0.25, 0.5, 1, 2, 4, 8, 16, 32],
        #'C' : [0.001, 0.01, 0.1, 1],
        'epsilon' : [0.1, 0.01, 0.05],
        'gamma' : ['auto'],
        'degree' : [2,3,4],
        'kernel' : ['sigmoid', 'linear', 'poly'],
        'coef0' : [0.0, 0.01, 0.05, 0.1]}


clf_svr = search_grid(svr, grid, X_train_3, y_train_3)


clf_svr.best_params_

{'C': 32,
 'coef0': 0.0,
 'degree': 2,
 'epsilon': 0.01,
 'gamma': 'auto',
 'kernel': 'linear'}


svr_results = pd.DataFrame(clf_svr.cv_results_)
svr_results.head(3)


optimised_svr = clf_svr.best_estimator_


joblib.dump(optimised_svr, 'svr_lag3_escalado.pkl')

['svr_lag3_escalado.pkl']


optimised_svr_lag3 = joblib.load('svr_lag3_escalado.pkl')


y_pred = optimised_svr_lag3.predict(X_test_3)


RMSEs_test_lag3_SVR = []
MAEs_test_lag3_SVR = []
R2s_test_lag3_SVR = []

RMSEs_test_lag3_SVR.append(RMSE(y_pred, y_test_3))
MAEs_test_lag3_SVR.append(MAE(y_pred, y_test_3))
R2s_test_lag3_SVR.append(R2(y_pred, y_test_3))

RMSE = 0.13
Mean absolute error = 0.09
R2 = 0.8


grafica(y_pred, y_test_3, -1)


predicciones_svr_lag3 = calcular_predicciones_lag3(optimised_svr_lag3, y_pred, X_test_3)


visualizar_predicciones(y_test_3, predicciones_svr_lag3)


MAE_SVR, RMSE_SVR, R2_SVR = graficas_errores(predicciones_svr_lag3, y_test_3)

Mean absolute error = 0.1
RMSE = 0.16
R2 = 0.69

Mean absolute error = 0.12
RMSE = 0.19
R2 = 0.58

Mean absolute error = 0.13
RMSE = 0.2
R2 = 0.53

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.38

Mean absolute error = 0.17
RMSE = 0.26
R2 = 0.23

Mean absolute error = 0.18
RMSE = 0.27
R2 = 0.12

Mean absolute error = 0.2
RMSE = 0.3
R2 = -0.04


for i in range(0, len(RMSE_SVR)):
    RMSEs_test_lag3_SVR.append(RMSE_SVR[i])
    MAEs_test_lag3_SVR.append(MAE_SVR[i])
    R2s_test_lag3_SVR.append(R2_SVR[i])


clf_svr = search_grid(svr, grid, X_train_5, y_train_5)


clf_svr.best_params_

{'C': 16,
 'coef0': 0.0,
 'degree': 2,
 'epsilon': 0.01,
 'gamma': 'auto',
 'kernel': 'linear'}


svr_results = pd.DataFrame(clf_svr.cv_results_)
svr_results.head(3)


optimised_svr = clf_svr.best_estimator_


joblib.dump(optimised_svr, 'svr_lag5_escalado.pkl')

['svr_lag5_escalado.pkl']


optimised_svr_lag5 = joblib.load('svr_lag5_escalado.pkl')


y_pred = optimised_svr_lag5.predict(X_test_5)


RMSEs_test_lag5_SVR = []
MAEs_test_lag5_SVR = []
R2s_test_lag5_SVR = []

RMSEs_test_lag5_SVR.append(RMSE(y_pred, y_test_5))
MAEs_test_lag5_SVR.append(MAE(y_pred, y_test_5))
R2s_test_lag5_SVR.append(R2(y_pred, y_test_5))

RMSE = 0.13
Mean absolute error = 0.08
R2 = 0.8


grafica(y_pred, y_test_5, -1)


predicciones_svr_lag5 = calcular_predicciones_lag5(optimised_svr_lag5, y_pred, X_test_5)


visualizar_predicciones(y_test_5, predicciones_svr_lag5)


MAE_SVR, RMSE_SVR, R2_SVR = graficas_errores(predicciones_svr_lag5, y_test_5)

Mean absolute error = 0.09
RMSE = 0.14
R2 = 0.77

Mean absolute error = 0.1
RMSE = 0.15
R2 = 0.73

Mean absolute error = 0.11
RMSE = 0.17
R2 = 0.67

Mean absolute error = 0.14
RMSE = 0.21
R2 = 0.5

Mean absolute error = 0.14
RMSE = 0.21
R2 = 0.47

Mean absolute error = 0.16
RMSE = 0.24
R2 = 0.32

Mean absolute error = 0.19
RMSE = 0.27
R2 = 0.13


for i in range(0, len(RMSE_SVR)):
    RMSEs_test_lag5_SVR.append(RMSE_SVR[i])
    MAEs_test_lag5_SVR.append(MAE_SVR[i])
    R2s_test_lag5_SVR.append(R2_SVR[i])


lasso = Lasso(random_state = 42)


lasso.get_params()

{'alpha': 1.0,
 'copy_X': True,
 'fit_intercept': True,
 'max_iter': 1000,
 'normalize': False,
 'positive': False,
 'precompute': False,
 'random_state': 42,
 'selection': 'cyclic',
 'tol': 0.0001,
 'warm_start': False}


grid = {#'alpha' : [0.9, 1, 1.1, 1.2, 1.3, 1.5],
        'alpha': np.logspace(-10, 3, 200),
        'max_iter' : [1000, 3000, 5000, 7000, 10000],
        'tol' : [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3, 0.5]}


clf_lasso = search_grid(lasso, grid, X_train_3, y_train_3)


clf_lasso.best_params_

{'alpha': 2.768286630392067e-06, 'max_iter': 3000, 'tol': 0.001}


lasso_results = pd.DataFrame(clf_lasso.cv_results_)#.sort_values(by = ['rank_test_score'])
#lasso_results.head(3)
#lasso_results[
#    ['params', 'rank_test_score', 'mean_test_score', 'std_test_score']]
lasso_results.head(3)


optimised_lasso = clf_lasso.best_estimator_


joblib.dump(optimised_lasso, 'lasso_lag3_escalado.pkl')

['lasso_lag3_escalado.pkl']


optimised_lasso_lag3 = joblib.load('lasso_lag3_escalado.pkl')


y_pred = optimised_lasso_lag3.predict(X_test_3)


RMSEs_test_lag3_lasso = []
MAEs_test_lag3_lasso = []
R2s_test_lag3_lasso = []

RMSEs_test_lag3_lasso.append(RMSE(y_pred, y_test_3))
MAEs_test_lag3_lasso.append(MAE(y_pred, y_test_3))
R2s_test_lag3_lasso.append(R2(y_pred, y_test_3))

RMSE = 0.13
Mean absolute error = 0.09
R2 = 0.79


grafica(y_pred, y_test_3, -1)


np.corrcoef(y_test_3, y_pred)

array([[1.        , 0.90318007],
       [0.90318007, 1.        ]])


predicciones_lasso_lag3 = calcular_predicciones_lag3(optimised_lasso_lag3, y_pred, X_test_3)


visualizar_predicciones(y_test_3, predicciones_lasso_lag3)


MAE_lasso, RMSE_lasso, R2_lasso = graficas_errores(predicciones_lasso_lag3, y_test_3)

Mean absolute error = 0.1
RMSE = 0.16
R2 = 0.67

Mean absolute error = 0.13
RMSE = 0.2
R2 = 0.54

Mean absolute error = 0.13
RMSE = 0.21
R2 = 0.49

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.36

Mean absolute error = 0.17
RMSE = 0.26
R2 = 0.23

Mean absolute error = 0.17
RMSE = 0.27
R2 = 0.16

Mean absolute error = 0.19
RMSE = 0.28
R2 = 0.05


for i in range(0, len(RMSE_lasso)):
    RMSEs_test_lag3_lasso.append(RMSE_lasso[i])
    MAEs_test_lag3_lasso.append(MAE_lasso[i])
    R2s_test_lag3_lasso.append(R2_lasso[i])


clf_lasso = search_grid(lasso, grid, X_train_5, y_train_5)


clf_lasso.best_params_

{'alpha': 2.768286630392067e-06, 'max_iter': 5000, 'tol': 0.001}


lasso_results = pd.DataFrame(clf_lasso.cv_results_)#.sort_values(by = ['rank_test_score'])
#lasso_results.head(3)
#lasso_results[
#    ['params', 'rank_test_score', 'mean_test_score', 'std_test_score']]
lasso_results.head(3)


optimised_lasso = clf_lasso.best_estimator_


joblib.dump(optimised_lasso, 'lasso_lag5_escalado.pkl')

['lasso_lag5_escalado.pkl']


optimised_lasso_lag5 = joblib.load('lasso_lag5_escalado.pkl')


y_pred = optimised_lasso_lag5.predict(X_test_5)


RMSEs_test_lag5_lasso = []
MAEs_test_lag5_lasso = []
R2s_test_lag5_lasso = []

RMSEs_test_lag5_lasso.append(RMSE(y_pred, y_test_5))
MAEs_test_lag5_lasso.append(MAE(y_pred, y_test_5))
R2s_test_lag5_lasso.append(R2(y_pred, y_test_5))

RMSE = 0.13
Mean absolute error = 0.08
R2 = 0.8


grafica(y_pred, y_test_5, -1)


np.corrcoef(y_test_5, y_pred)

array([[1.        , 0.90748449],
       [0.90748449, 1.        ]])


predicciones_lasso_lag5 = calcular_predicciones_lag5(optimised_lasso_lag5, y_pred, X_test_5)


visualizar_predicciones(y_test_5, predicciones_lasso_lag5)


MAE_lasso, RMSE_lasso, R2_lasso = graficas_errores(predicciones_lasso_lag5, y_test_5)

Mean absolute error = 0.09
RMSE = 0.14
R2 = 0.75

Mean absolute error = 0.11
RMSE = 0.16
R2 = 0.69

Mean absolute error = 0.12
RMSE = 0.18
R2 = 0.61

Mean absolute error = 0.15
RMSE = 0.22
R2 = 0.44

Mean absolute error = 0.16
RMSE = 0.23
R2 = 0.38

Mean absolute error = 0.17
RMSE = 0.25
R2 = 0.24

Mean absolute error = 0.19
RMSE = 0.28
R2 = 0.09


for i in range(0, len(RMSE_lasso)):
    RMSEs_test_lag5_lasso.append(RMSE_lasso[i])
    MAEs_test_lag5_lasso.append(MAE_lasso[i])
    R2s_test_lag5_lasso.append(R2_lasso[i])


# Instancia del modelo
gbr = GradientBoostingRegressor(random_state = 42)


gbr.get_params()

{'alpha': 0.9,
 'ccp_alpha': 0.0,
 'criterion': 'friedman_mse',
 'init': None,
 'learning_rate': 0.1,
 'loss': 'ls',
 'max_depth': 3,
 'max_features': None,
 'max_leaf_nodes': None,
 'min_impurity_decrease': 0.0,
 'min_impurity_split': None,
 'min_samples_leaf': 1,
 'min_samples_split': 2,
 'min_weight_fraction_leaf': 0.0,
 'n_estimators': 100,
 'n_iter_no_change': None,
 'presort': 'deprecated',
 'random_state': 42,
 'subsample': 1.0,
 'tol': 0.0001,
 'validation_fraction': 0.1,
 'verbose': 0,
 'warm_start': False}


grid = {'n_estimators':[50, 100, 250, 500], 
        'max_depth':[3, 4, 5, 8, 10], 
        'min_samples_leaf':[4, 8, 10], 
        'max_features':[2, 3, 5]}


clf_gbr = search_grid(gbr, grid, X_train_3, y_train_3)


clf_gbr.best_params_

{'max_depth': 3, 'max_features': 5, 'min_samples_leaf': 4, 'n_estimators': 50}


gbr_results = pd.DataFrame(clf_gbr.cv_results_)
gbr_results.head(3)


optimised_gbr = clf_gbr.best_estimator_


joblib.dump(optimised_gbr, 'gbr_lag3_escalado.pkl')

['gbr_lag3_escalado.pkl']


optimised_gbr_lag3 = joblib.load('gbr_lag3_escalado.pkl')


y_pred = optimised_gbr_lag3.predict(X_test_3)


RMSEs_test_lag3_gbr = []
MAEs_test_lag3_gbr = []
R2s_test_lag3_gbr = []

RMSEs_test_lag3_gbr.append(RMSE(y_pred, y_test_3))
MAEs_test_lag3_gbr.append(MAE(y_pred, y_test_3))
R2s_test_lag3_gbr.append(R2(y_pred, y_test_3))

RMSE = 0.21
Mean absolute error = 0.13
R2 = 0.45


grafica(y_pred, y_test_3, -1)


np.corrcoef(y_test_3, y_pred)

array([[1.       , 0.8521839],
       [0.8521839, 1.       ]])


predicciones_gbr_lag3 = calcular_predicciones_lag3(optimised_gbr_lag3, y_pred, X_test_3)


visualizar_predicciones(y_test_3, predicciones_gbr_lag3)


MAE_gbr, RMSE_gbr, R2_gbr = graficas_errores(predicciones_gbr_lag3, y_test_3)

Mean absolute error = 0.14
RMSE = 0.22
R2 = 0.39

Mean absolute error = 0.14
RMSE = 0.22
R2 = 0.4

Mean absolute error = 0.14
RMSE = 0.23
R2 = 0.39

Mean absolute error = 0.14
RMSE = 0.23
R2 = 0.39

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.38

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.38

Mean absolute error = 0.15
RMSE = 0.23
R2 = 0.37


for i in range(0, len(RMSE_gbr)):
    RMSEs_test_lag3_gbr.append(RMSE_gbr[i])
    MAEs_test_lag3_gbr.append(MAE_gbr[i])
    R2s_test_lag3_gbr.append(R2_gbr[i])


clf_gbr = search_grid(gbr, grid, X_train_5, y_train_5)


clf_gbr.best_params_

{'max_depth': 10,
 'max_features': 5,
 'min_samples_leaf': 10,
 'n_estimators': 50}


gbr_results = pd.DataFrame(clf_gbr.cv_results_)
gbr_results.head(3)


optimised_gbr = clf_gbr.best_estimator_


joblib.dump(optimised_gbr, 'gbr_lag5_escalado.pkl')

['gbr_lag5_escalado.pkl']


optimised_gbr_lag5 = joblib.load('gbr_lag5_escalado.pkl')


y_pred = optimised_gbr_lag5.predict(X_test_5)


RMSEs_test_lag5_gbr = []
MAEs_test_lag5_gbr = []
R2s_test_lag5_gbr = []

RMSEs_test_lag5_gbr.append(RMSE(y_pred, y_test_5))
MAEs_test_lag5_gbr.append(MAE(y_pred, y_test_5))
R2s_test_lag5_gbr.append(R2(y_pred, y_test_5))

RMSE = 0.21
Mean absolute error = 0.13
R2 = 0.46


grafica(y_pred, y_test_5, -1)


np.corrcoef(y_test_5, y_pred)

array([[1.        , 0.85991689],
       [0.85991689, 1.        ]])


predicciones_gbr_lag5 = calcular_predicciones_lag5(optimised_gbr_lag5, y_pred, X_test_5)


visualizar_predicciones(y_test_5, predicciones_gbr_lag5)


MAE_gbr, RMSE_gbr, R2_gbr = graficas_errores(predicciones_gbr_lag5, y_test_5)

Mean absolute error = 0.14
RMSE = 0.21
R2 = 0.44

Mean absolute error = 0.14
RMSE = 0.22
R2 = 0.44

Mean absolute error = 0.14
RMSE = 0.22
R2 = 0.44

Mean absolute error = 0.14
RMSE = 0.22
R2 = 0.44

Mean absolute error = 0.14
RMSE = 0.22
R2 = 0.44

Mean absolute error = 0.14
RMSE = 0.22
R2 = 0.43

Mean absolute error = 0.14
RMSE = 0.22
R2 = 0.42


for i in range(0, len(RMSE_gbr)):
    RMSEs_test_lag5_gbr.append(RMSE_gbr[i])
    MAEs_test_lag5_gbr.append(MAE_gbr[i])
    R2s_test_lag5_gbr.append(R2_gbr[i])


elastic_net = ElasticNet(random_state = 42)


grid = {'alpha' : [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 0.0, 1.0, 10.0, 100.0],
        'l1_ratio' : np.arange(0, 1, 0.01),
        'max_iter' : [10000,15000,20000],
        'tol' : [0.0001, 0.001, 0.01, 0.1, 0.2, 0.3, 0.5]}


clf_en = search_grid(elastic_net, grid, X_train_3, y_train_3)


clf_en.best_params_

{'alpha': 1e-05, 'l1_ratio': 0.17, 'max_iter': 10000, 'tol': 0.001}


en_results = pd.DataFrame(clf_en.cv_results_)
en_results.head(3)


optimised_en = clf_en.best_estimator_


joblib.dump(optimised_en, 'en_lag3_escalado.pkl')

['en_lag3_escalado.pkl']


optimised_en_lag3 = joblib.load('en_lag3_escalado.pkl')


y_pred = optimised_en_lag3.predict(X_test_3)


RMSEs_test_lag3_en = []
MAEs_test_lag3_en = []
R2s_test_lag3_en = []

RMSEs_test_lag3_en.append(RMSE(y_pred, y_test_3))
MAEs_test_lag3_en.append(MAE(y_pred, y_test_3))
R2s_test_lag3_en.append(R2(y_pred, y_test_3))

RMSE = 0.13
Mean absolute error = 0.09
R2 = 0.79


grafica(y_pred, y_test_3, -1)


np.corrcoef(y_test_3, y_pred)

array([[1.        , 0.90394023],
       [0.90394023, 1.        ]])


predicciones_en_lag3 = calcular_predicciones_lag3(optimised_en_lag3, y_pred, X_test_3)


visualizar_predicciones(y_test_3, predicciones_en_lag3)


MAE_en, RMSE_en, R2_en = graficas_errores(predicciones_en_lag3, y_test_3)

Mean absolute error = 0.1
RMSE = 0.16
R2 = 0.69

Mean absolute error = 0.12
RMSE = 0.19
R2 = 0.58

Mean absolute error = 0.13
RMSE = 0.2
R2 = 0.53

Mean absolute error = 0.14
RMSE = 0.22
R2 = 0.43

Mean absolute error = 0.16
RMSE = 0.24
R2 = 0.33

Mean absolute error = 0.17
RMSE = 0.25
R2 = 0.27

Mean absolute error = 0.17
RMSE = 0.26
R2 = 0.19


for i in range(0, len(RMSE_en)):
    RMSEs_test_lag3_en.append(RMSE_en[i])
    MAEs_test_lag3_en.append(MAE_en[i])
    R2s_test_lag3_en.append(R2_en[i])


clf_en = search_grid(elastic_net, grid, X_train_5, y_train_5)


clf_en.best_params_

{'alpha': 1e-05, 'l1_ratio': 0.13, 'max_iter': 10000, 'tol': 0.0001}


en_results = pd.DataFrame(clf_en.cv_results_)
en_results.head(3)


optimised_en = clf_en.best_estimator_


joblib.dump(optimised_en, 'en_lag5_escalado.pkl')

['en_lag5_escalado.pkl']


optimised_en_lag5 = joblib.load('en_lag5_escalado.pkl')


y_pred = optimised_en_lag5.predict(X_test_5)


RMSEs_test_lag5_en = []
MAEs_test_lag5_en = []
R2s_test_lag5_en = []

RMSEs_test_lag5_en.append(RMSE(y_pred, y_test_5))
MAEs_test_lag5_en.append(MAE(y_pred, y_test_5))
R2s_test_lag5_en.append(R2(y_pred, y_test_5))

RMSE = 0.13
Mean absolute error = 0.08
R2 = 0.8


grafica(y_pred, y_test_5, -1)


np.corrcoef(y_test_5, y_pred)

array([[1.        , 0.90821593],
       [0.90821593, 1.        ]])


predicciones_en_lag5 = calcular_predicciones_lag5(optimised_en_lag5, y_pred, X_test_5)


visualizar_predicciones(y_test_5, predicciones_en_lag5)


MAE_en, RMSE_en, R2_en = graficas_errores(predicciones_en_lag5, y_test_5)

Mean absolute error = 0.09
RMSE = 0.14
R2 = 0.77

Mean absolute error = 0.1
RMSE = 0.15
R2 = 0.71

Mean absolute error = 0.11
RMSE = 0.17
R2 = 0.64

Mean absolute error = 0.14
RMSE = 0.21
R2 = 0.48

Mean absolute error = 0.15
RMSE = 0.22
R2 = 0.42

Mean absolute error = 0.17
RMSE = 0.24
R2 = 0.3

Mean absolute error = 0.19
RMSE = 0.27
R2 = 0.16


for i in range(0, len(RMSE_en)):
    RMSEs_test_lag5_en.append(RMSE_en[i])
    MAEs_test_lag5_en.append(MAE_en[i])
    R2s_test_lag5_en.append(R2_en[i])


cv = TimeSeriesSplit(n_splits = 10)


scorings = ['r2', 'neg_root_mean_squared_error', 'neg_median_absolute_error']


def obtener_r2(scores):
    return scores['test_r2']


def obtener_rmse(scores):
    return (-1) * scores['test_neg_root_mean_squared_error']


def obtener_mae(scores):
    return (-1) * scores['test_neg_median_absolute_error']


RMSEs_train = []
R2s_train = []
MAEs_train = []


columnas = ['RF_lag3', 'RF_lag5', 'LR_lag3', 'LR_lag5', 'Lasso_lag3', 'Lasso_lag5', 'SVR_lag3', 'SVR_lag5', 'GBR_lag3', 'GBR_lag5', 'EN_lag3', 'EN_lag5' ]


filas_train = []
for i in range(1,11):
    filas_train.append('Fold ' + str(i))


def crear_dataframe_error_train(columnas, filas, errores):
    df = pd.DataFrame(0, columns = columnas, index = filas)
    df.index.name = 'Pliegues'
    for i in range(0, 12):
        df.iloc[:,i] = errores[i]
    return df


optimised_rf_lag3 = joblib.load('rf_lag3_escalado.pkl')
optimised_rf_lag5 = joblib.load('rf_lag5_escalado.pkl')

scores_RF_lag3 = cross_validate(optimised_rf_lag3, X_train_3, y_train_3, cv = cv, scoring = scorings)
scores_RF_lag5 = cross_validate(optimised_rf_lag5, X_train_5, y_train_5, cv = cv, scoring = scorings)

R2s_train.append(obtener_r2(scores_RF_lag3))
RMSEs_train.append(obtener_rmse(scores_RF_lag3))
MAEs_train.append(obtener_mae(scores_RF_lag3))

R2s_train.append(obtener_r2(scores_RF_lag5))
RMSEs_train.append(obtener_rmse(scores_RF_lag5))
MAEs_train.append(obtener_mae(scores_RF_lag5))


regresion_lineal = LinearRegression()

scores_LR_lag3 = cross_validate(regresion_lineal, X_train_3, y_train_3, cv = cv, scoring = scorings)
scores_LR_lag5 = cross_validate(regresion_lineal, X_train_5, y_train_5, cv = cv, scoring = scorings)

R2s_train.append(obtener_r2(scores_LR_lag3))
RMSEs_train.append(obtener_rmse(scores_LR_lag3))
MAEs_train.append(obtener_mae(scores_LR_lag3))

R2s_train.append(obtener_r2(scores_LR_lag5))
RMSEs_train.append(obtener_rmse(scores_LR_lag5))
MAEs_train.append(obtener_mae(scores_LR_lag5))


optimised_lasso_lag3 = joblib.load('lasso_lag3_escalado.pkl')
optimised_lasso_lag5 = joblib.load('lasso_lag5_escalado.pkl')

scores_lasso_lag3 = cross_validate(optimised_lasso_lag3, X_train_3, y_train_3, cv = cv, scoring = scorings)
scores_lasso_lag5 = cross_validate(optimised_lasso_lag5, X_train_5, y_train_5, cv = cv, scoring = scorings)

R2s_train.append(obtener_r2(scores_lasso_lag3))
RMSEs_train.append(obtener_rmse(scores_lasso_lag3))
MAEs_train.append(obtener_mae(scores_lasso_lag3))

R2s_train.append(obtener_r2(scores_lasso_lag5))
RMSEs_train.append(obtener_rmse(scores_lasso_lag5))
MAEs_train.append(obtener_mae(scores_lasso_lag5))


optimised_svr_lag3 = joblib.load('svr_lag3_escalado.pkl')
optimised_svr_lag5 = joblib.load('svr_lag5_escalado.pkl')

scores_svr_lag3 = cross_validate(optimised_svr_lag3, X_train_3, y_train_3, cv = cv, scoring = scorings)
scores_svr_lag5 = cross_validate(optimised_svr_lag5, X_train_5, y_train_5, cv = cv, scoring = scorings)

R2s_train.append(obtener_r2(scores_svr_lag3))
RMSEs_train.append(obtener_rmse(scores_svr_lag3))
MAEs_train.append(obtener_mae(scores_svr_lag3))

R2s_train.append(obtener_r2(scores_svr_lag5))
RMSEs_train.append(obtener_rmse(scores_svr_lag5))
MAEs_train.append(obtener_mae(scores_svr_lag5))


optimised_gbr_lag3 = joblib.load('gbr_lag3_escalado.pkl')
optimised_gbr_lag5 = joblib.load('gbr_lag5_escalado.pkl')

scores_gbr_lag3 = cross_validate(optimised_gbr_lag3, X_train_3, y_train_3, cv = cv, scoring = scorings)
scores_gbr_lag5 = cross_validate(optimised_gbr_lag5, X_train_5, y_train_5, cv = cv, scoring = scorings)

R2s_train.append(obtener_r2(scores_gbr_lag3))
RMSEs_train.append(obtener_rmse(scores_gbr_lag3))
MAEs_train.append(obtener_mae(scores_gbr_lag3))

R2s_train.append(obtener_r2(scores_gbr_lag5))
RMSEs_train.append(obtener_rmse(scores_gbr_lag5))
MAEs_train.append(obtener_mae(scores_gbr_lag5))


optimised_en_lag3 = joblib.load('en_lag3_escalado.pkl')
optimised_en_lag5 = joblib.load('en_lag5_escalado.pkl')

scores_en_lag3 = cross_validate(optimised_en_lag3, X_train_3, y_train_3, cv = cv, scoring = scorings)
scores_en_lag5 = cross_validate(optimised_en_lag5, X_train_5, y_train_5, cv = cv, scoring = scorings)

R2s_train.append(obtener_r2(scores_en_lag3))
RMSEs_train.append(obtener_rmse(scores_en_lag3))
MAEs_train.append(obtener_mae(scores_en_lag3))

R2s_train.append(obtener_r2(scores_en_lag5))
RMSEs_train.append(obtener_rmse(scores_en_lag5))
MAEs_train.append(obtener_mae(scores_en_lag5))


RMSE_errores_train = crear_dataframe_error_train(columnas, filas_train, RMSEs_train)
RMSE_errores_train


RMSE_errores_train.to_csv(path_or_buf = '../Datos_preprocesados/RMSE_errores_train.csv')


RMSE_errores_train = read_csv('../Datos_preprocesados/RMSE_errores_train.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
RMSE_errores_train = RMSE_errores_train.set_index('Pliegues')


MAE_errores_train = crear_dataframe_error_train(columnas, filas_train, MAEs_train)
MAE_errores_train


MAE_errores_train.to_csv(path_or_buf = '../Datos_preprocesados/MAE_errores_train.csv')


MAE_errores_train = read_csv('../Datos_preprocesados/MAE_errores_train.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
MAE_errores_train = MAE_errores_train.set_index('Pliegues')


R2_errores_train = crear_dataframe_error_train(columnas, filas_train, R2s_train)
R2_errores_train


R2_errores_train.to_csv(path_or_buf = '../Datos_preprocesados/R2_errores_train.csv')


R2_errores_train = read_csv('../Datos_preprocesados/R2_errores_train.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
R2_errores_train = R2_errores_train.set_index('Pliegues')


columnas = ['RF_lag3', 'RF_lag5', 'LR_lag3', 'LR_lag5', 'Lasso_lag3', 'Lasso_lag5', 'SVR_lag3', 'SVR_lag5', 'GBR_lag3', 'GBR_lag5', 'EN_lag3', 'EN_lag5' ]


filas_test = []
for i in range(1,9):
    if i-1 == 0:
        filas_test.append('Predicción t')
    else:
        filas_test.append('Predicción t+' + str(i-1))


def crear_dataframe_error_test(columnas, filas, errores):
    df = pd.DataFrame(0, columns = columnas, index = filas)
    df.index.name = 'Predicciones'
    i = 0
    for c in df.columns:
        df[c] = errores[i]
        i = i+1
    return df


RMSEs_test = []
RMSEs_test.append(RMSEs_test_lag3_rf)
RMSEs_test.append(RMSEs_test_lag5_rf)
RMSEs_test.append(RMSEs_test_lag3_LR)
RMSEs_test.append(RMSEs_test_lag5_LR)
RMSEs_test.append(RMSEs_test_lag3_lasso)
RMSEs_test.append(RMSEs_test_lag5_lasso)
RMSEs_test.append(RMSEs_test_lag3_SVR)
RMSEs_test.append(RMSEs_test_lag5_SVR)
RMSEs_test.append(RMSEs_test_lag3_gbr)
RMSEs_test.append(RMSEs_test_lag5_gbr)
RMSEs_test.append(RMSEs_test_lag3_en)
RMSEs_test.append(RMSEs_test_lag5_en)


RMSE_errores_test = crear_dataframe_error_test(columnas, filas_test, RMSEs_test)
RMSE_errores_test


RMSE_errores_test.to_csv(path_or_buf = '../Datos_preprocesados/RMSE_errores_test.csv')


RMSE_errores_test = read_csv('../Datos_preprocesados/RMSE_errores_test.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
RMSE_errores_test = RMSE_errores_test.set_index('Predicciones')


MAEs_test = []
MAEs_test.append(MAEs_test_lag3_rf)
MAEs_test.append(MAEs_test_lag5_rf)
MAEs_test.append(MAEs_test_lag3_LR)
MAEs_test.append(MAEs_test_lag5_LR)
MAEs_test.append(MAEs_test_lag3_lasso)
MAEs_test.append(MAEs_test_lag5_lasso)
MAEs_test.append(MAEs_test_lag3_SVR)
MAEs_test.append(MAEs_test_lag5_SVR)
MAEs_test.append(MAEs_test_lag3_gbr)
MAEs_test.append(MAEs_test_lag5_gbr)
MAEs_test.append(MAEs_test_lag3_en)
MAEs_test.append(MAEs_test_lag5_en)


MAE_errores_test = crear_dataframe_error_test(columnas, filas_test, MAEs_test)
MAE_errores_test


MAE_errores_test.to_csv(path_or_buf = '../Datos_preprocesados/MAE_errores_test.csv')


MAE_errores_test = read_csv('../Datos_preprocesados/MAE_errores_test.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
MAE_errores_test = MAE_errores_test.set_index('Predicciones')


R2s_test = []
R2s_test.append(R2s_test_lag3_rf)
R2s_test.append(R2s_test_lag5_rf)
R2s_test.append(R2s_test_lag3_LR)
R2s_test.append(R2s_test_lag5_LR)
R2s_test.append(R2s_test_lag3_lasso)
R2s_test.append(R2s_test_lag5_lasso)
R2s_test.append(R2s_test_lag3_SVR)
R2s_test.append(R2s_test_lag5_SVR)
R2s_test.append(R2s_test_lag3_gbr)
R2s_test.append(R2s_test_lag5_gbr)
R2s_test.append(R2s_test_lag3_en)
R2s_test.append(R2s_test_lag5_en)


R2_errores_test = crear_dataframe_error_test(columnas, filas_test, R2s_test)
R2_errores_test


R2_errores_test.to_csv(path_or_buf = '../Datos_preprocesados/R2_errores_test.csv')


R2_errores_test = read_csv('../Datos_preprocesados/R2_errores_test.csv', encoding='latin-1', sep = ',', na_values = ['NaN', 'NaT'])
R2_errores_test = R2_errores_test.set_index('Predicciones')

	Entradas	Salidas
Fecha
2021-02-26	130495.728	126471.246
2021-02-27	73285.851	71385.928
2021-02-28	49817.997	49735.536

	Incidentes	Pruebas t-1	Pruebas t-2	Pruebas t-3	Incidentes t-1	Incidentes t-2	Incidentes t-3	H t-1	H t-2	H t-3	...	Asintomaticos t-3	Sintomaticos t-1	Sintomaticos t-2	Sintomaticos t-3	Entradas t-1	Entradas t-2	Entradas t-3	Salidas t-1	Salidas t-2	Salidas t-3
Fecha
2020-03-11	12.0	39.0	21.0	130.0	6.0	3.0	1.0	0.0	0.0	0.0	...	2.0	4.0	4.0	1.0	145999.438	146586.006	134872.118	145006.937	148231.629	129792.956
2020-03-12	9.0	116.0	39.0	21.0	12.0	6.0	3.0	0.0	0.0	0.0	...	0.0	10.0	4.0	4.0	145431.106	145999.438	146586.006	143921.173	145006.937	148231.629
2020-03-13	12.0	136.0	116.0	39.0	9.0	12.0	6.0	4.0	0.0	0.0	...	0.0	7.0	10.0	4.0	145470.771	145431.106	145999.438	144238.438	143921.173	145006.937

	Incidentes	Pruebas t-1	Pruebas t-2	Pruebas t-3	Incidentes t-1	Incidentes t-2	Incidentes t-3	H t-1	H t-2	H t-3	...	Asintomaticos t-3	Sintomaticos t-1	Sintomaticos t-2	Sintomaticos t-3	Entradas t-1	Entradas t-2	Entradas t-3	Salidas t-1	Salidas t-2	Salidas t-3
Fecha
2020-03-11	12.0	39.0	21.0	130.0	6.0	3.0	1.0	0.0	0.0	0.0	...	2.0	4.0	4.0	1.0	145999.438	146586.006	134872.118	145006.937	148231.629	129792.956
2020-03-12	9.0	116.0	39.0	21.0	12.0	6.0	3.0	0.0	0.0	0.0	...	0.0	10.0	4.0	4.0	145431.106	145999.438	146586.006	143921.173	145006.937	148231.629
2020-03-13	12.0	136.0	116.0	39.0	9.0	12.0	6.0	4.0	0.0	0.0	...	0.0	7.0	10.0	4.0	145470.771	145431.106	145999.438	144238.438	143921.173	145006.937

	Incidentes	Pruebas t-1	Pruebas t-2	Pruebas t-3	Pruebas t-4	Pruebas t-5	Incidentes t-1	Incidentes t-2	Incidentes t-3	Incidentes t-4	...	Entradas t-1	Entradas t-2	Entradas t-3	Entradas t-4	Entradas t-5	Salidas t-1	Salidas t-2	Salidas t-3	Salidas t-4	Salidas t-5
Fecha
2020-03-13	12.0	136.0	116.0	39.0	21.0	130.0	9.0	12.0	6.0	3.0	...	145470.771	145431.106	145999.438	146586.006	134872.118	144238.438	143921.173	145006.937	148231.629	129792.956
2020-03-14	23.0	228.0	136.0	116.0	39.0	21.0	12.0	9.0	12.0	6.0	...	143647.028	145470.771	145431.106	145999.438	146586.006	140719.734	144238.438	143921.173	145006.937	148231.629
2020-03-15	18.0	196.0	228.0	136.0	116.0	39.0	23.0	12.0	9.0	12.0	...	71089.567	143647.028	145470.771	145431.106	145999.438	69784.739	140719.734	144238.438	143921.173	145006.937

	Incidentes	Pruebas t-1	Pruebas t-2	Pruebas t-3	Pruebas t-4	Pruebas t-5	Incidentes t-1	Incidentes t-2	Incidentes t-3	Incidentes t-4	...	Entradas t-1	Entradas t-2	Entradas t-3	Entradas t-4	Entradas t-5	Salidas t-1	Salidas t-2	Salidas t-3	Salidas t-4	Salidas t-5
Fecha
2020-03-13	12.0	136.0	116.0	39.0	21.0	130.0	9.0	12.0	6.0	3.0	...	145470.771	145431.106	145999.438	146586.006	134872.118	144238.438	143921.173	145006.937	148231.629	129792.956
2020-03-14	23.0	228.0	136.0	116.0	39.0	21.0	12.0	9.0	12.0	6.0	...	143647.028	145470.771	145431.106	145999.438	146586.006	140719.734	144238.438	143921.173	145006.937	148231.629
2020-03-15	18.0	196.0	228.0	136.0	116.0	39.0	23.0	12.0	9.0	12.0	...	71089.567	143647.028	145470.771	145431.106	145999.438	69784.739	140719.734	144238.438	143921.173	145006.937

	Fecha	Pruebas	Incidentes	Asint_H_<45	Sint_H_<45	Sint_H_45a64	Sint_H_+65	Asint_M_<45	Sint_M_<45
0	08/03/2020	130.0	1.0	1.0	0.0	0.0	0.0	1.0	1.0
1	09/03/2020	21.0	3.0	0.0	0.0	1.0	0.0	0.0	3.0
2	10/03/2020	39.0	6.0	0.0	1.0	0.0	1.0	0.0	2.0

	Pruebas	Incidentes	H	H_UCI	Defunciones	Asint_H_<45	Asint_H_45a64	Asint_H_+65	Sint_H_<45	Sint_H_45a64	Sint_H_+65	Asint_M_<45	Asint_M_45a64	Asint_M_+65	Sint_M_<45	Sint_M_45a64	Sint_M_+65
Fecha
2020-03-08	130.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0
2020-03-09	21.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	3.0	0.0	0.0
2020-03-10	39.0	6.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.0	2.0	0.0	0.0

	Pruebas	Incidentes	H	H_UCI	Defunciones	Asintomaticos	Sintomaticos	Entradas	Salidas
Fecha
2020-02-21	NaN	NaN	NaN	NaN	NaN	NaN	NaN	166795.814	168567.936
2020-02-22	NaN	NaN	NaN	NaN	NaN	NaN	NaN	144424.897	144077.722
2020-02-23	NaN	NaN	NaN	NaN	NaN	NaN	NaN	138142.960	133974.435

	Incidentes	Pruebas t-1	Pruebas t-2	Pruebas t-3	Incidentes t-1	Incidentes t-2	Incidentes t-3	H t-1	H t-2	H t-3	...	Asintomaticos t-3	Sintomaticos t-1	Sintomaticos t-2	Sintomaticos t-3	Entradas t-1	Entradas t-2	Entradas t-3	Salidas t-1	Salidas t-2	Salidas t-3
Fecha
2020-03-11	0.005256	0.003270	0.001761	0.010901	0.002628	0.001314	0.000438	0.000000	0.0	0.0	...	0.00354	0.002415	0.002415	0.000604	0.371644	0.373381	0.338683	0.335522	0.344440	0.293448
2020-03-12	0.003942	0.009727	0.003270	0.001761	0.005256	0.002628	0.001314	0.000000	0.0	0.0	...	0.00000	0.006039	0.002415	0.002415	0.369960	0.371644	0.373381	0.332519	0.335522	0.344440
2020-03-13	0.005256	0.011404	0.009727	0.003270	0.003942	0.005256	0.002628	0.003521	0.0	0.0	...	0.00000	0.004227	0.006039	0.002415	0.370078	0.369960	0.371644	0.333397	0.332519	0.335522

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_bootstrap	param_max_depth	param_max_features	param_min_samples_leaf	param_min_samples_split	param_n_estimators	...	split2_train_score	split3_train_score	split4_train_score	split5_train_score	split6_train_score	split7_train_score	split8_train_score	split9_train_score	mean_train_score	std_train_score
0	0.230483	0.010087	0.013764	0.001247	True	20	2	1	4	100	...	-0.002737	-0.002415	-0.002494	-0.003163	-0.005832	-0.008506	-0.012143	-0.017030	-0.006214	0.004670
1	0.822700	0.049547	0.047374	0.008924	True	20	2	1	4	300	...	-0.002774	-0.002479	-0.002548	-0.003127	-0.005801	-0.008935	-0.012458	-0.017392	-0.006318	0.004814
2	1.210761	0.127360	0.057546	0.009420	True	20	2	1	4	500	...	-0.002825	-0.002459	-0.002506	-0.003088	-0.005762	-0.008826	-0.012337	-0.017481	-0.006297	0.004816

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_bootstrap	param_max_depth	param_max_features	param_min_samples_leaf	param_min_samples_split	param_n_estimators	...	split2_train_score	split3_train_score	split4_train_score	split5_train_score	split6_train_score	split7_train_score	split8_train_score	split9_train_score	mean_train_score	std_train_score
0	0.238150	0.032929	0.015190	0.000892	True	20	2	1	4	100	...	-0.002989	-0.002695	-0.002551	-0.003324	-0.006114	-0.008909	-0.012888	-0.017250	-0.006460	0.004775
1	0.714150	0.050421	0.033534	0.007744	True	20	2	1	4	300	...	-0.002869	-0.002493	-0.002467	-0.003173	-0.005845	-0.008784	-0.012696	-0.017192	-0.006311	0.004789
2	1.208178	0.081138	0.063989	0.010854	True	20	2	1	4	500	...	-0.002908	-0.002511	-0.002490	-0.003147	-0.005818	-0.009112	-0.012635	-0.017075	-0.006328	0.004769

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_C	param_degree	param_epsilon	param_gamma	param_kernel	...	split2_train_score	split3_train_score	split4_train_score	split5_train_score	split6_train_score	split7_train_score	split8_train_score	split9_train_score	mean_train_score	std_train_score
0	0.004388	0.001111	0.003491	0.002241	0.25	2	0.1	auto	sigmoid	...	-0.020984	-0.021895	-0.021811	-0.027055	-0.086278	-0.084233	-0.079713	-0.081937	-0.045886	0.030491
1	0.004787	0.002742	0.007481	0.008582	0.25	2	0.1	auto	linear	...	-0.020984	-0.021895	-0.021811	-0.027055	-0.085420	-0.070687	-0.040356	-0.057981	-0.038114	0.023457
2	0.003989	0.000892	0.006084	0.006517	0.25	2	0.1	auto	poly	...	-0.020984	-0.021895	-0.021811	-0.027055	-0.086582	-0.088656	-0.097484	-0.098198	-0.049762	0.035335

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_C	param_degree	param_epsilon	param_gamma	param_kernel	...	split2_train_score	split3_train_score	split4_train_score	split5_train_score	split6_train_score	split7_train_score	split8_train_score	split9_train_score	mean_train_score	std_train_score
0	0.006470	0.004916	0.003191	3.989541e-04	0.25	2	0.1	auto	sigmoid	...	-0.020972	-0.021903	-0.021816	-0.02703	-0.086462	-0.084307	-0.081021	-0.083449	-0.046131	0.030912
1	0.005087	0.001509	0.006483	6.529154e-03	0.25	2	0.1	auto	linear	...	-0.020972	-0.021903	-0.021816	-0.02703	-0.086055	-0.075169	-0.040656	-0.056998	-0.038495	0.024216
2	0.007779	0.008186	0.002992	9.419797e-07	0.25	2	0.1	auto	poly	...	-0.020972	-0.021903	-0.021816	-0.02703	-0.086306	-0.088386	-0.098377	-0.098862	-0.049800	0.035548

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_alpha	param_max_iter	param_tol	params	split0_test_score	split1_test_score	...	split2_train_score	split3_train_score	split4_train_score	split5_train_score	split6_train_score	split7_train_score	split8_train_score	split9_train_score	mean_train_score	std_train_score
0	0.013264	0.001003	0.003292	0.000458	1e-10	1000	0.0001	{'alpha': 1e-10, 'max_iter': 1000, 'tol': 0.0001}	-0.017193	-0.005675	...	-0.003463	-0.00318	-0.003783	-0.006043	-0.01041	-0.017316	-0.023809	-0.031761	-0.010616	0.009749
1	0.013962	0.001945	0.003890	0.001509	1e-10	1000	0.001	{'alpha': 1e-10, 'max_iter': 1000, 'tol': 0.001}	-0.017193	-0.005675	...	-0.003463	-0.00318	-0.003783	-0.006043	-0.01041	-0.017316	-0.023809	-0.031761	-0.010616	0.009749
2	0.015558	0.003065	0.003590	0.001111	1e-10	1000	0.01	{'alpha': 1e-10, 'max_iter': 1000, 'tol': 0.01}	-0.017193	-0.005675	...	-0.003463	-0.00318	-0.003783	-0.006043	-0.01041	-0.017316	-0.023809	-0.031761	-0.010616	0.009749

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_alpha	param_max_iter	param_tol	params	split0_test_score	split1_test_score	...	split2_train_score	split3_train_score	split4_train_score	split5_train_score	split6_train_score	split7_train_score	split8_train_score	split9_train_score	mean_train_score	std_train_score
0	0.025032	0.009633	0.004290	0.001611	1e-10	1000	0.0001	{'alpha': 1e-10, 'max_iter': 1000, 'tol': 0.0001}	-0.024179	-0.007147	...	-0.002734	-0.00264	-0.003072	-0.00512	-0.008976	-0.014611	-0.021445	-0.029719	-0.009168	0.009241
1	0.021642	0.004325	0.004389	0.001351	1e-10	1000	0.001	{'alpha': 1e-10, 'max_iter': 1000, 'tol': 0.001}	-0.024179	-0.007147	...	-0.002734	-0.00264	-0.003072	-0.00512	-0.008976	-0.014611	-0.021445	-0.029719	-0.009168	0.009241
2	0.020645	0.007261	0.002992	0.000446	1e-10	1000	0.01	{'alpha': 1e-10, 'max_iter': 1000, 'tol': 0.01}	-0.018363	-0.007147	...	-0.002734	-0.00264	-0.003072	-0.00512	-0.008976	-0.014611	-0.021445	-0.029719	-0.009258	0.009163

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_max_depth	param_max_features	param_min_samples_leaf	param_n_estimators	params	split0_test_score	...	split2_train_score	split3_train_score	split4_train_score	split5_train_score	split6_train_score	split7_train_score	split8_train_score	split9_train_score	mean_train_score	std_train_score
0	0.026728	0.003301	0.003591	0.000488	3	2	4	50	{'max_depth': 3, 'max_features': 2, 'min_sampl...	-0.011109	...	-0.000984	-0.000940	-0.001136	-0.001857	-0.002766	-0.004857	-0.007097	-0.010549	-0.003219	0.003128
1	0.048071	0.005330	0.003292	0.000457	3	2	4	100	{'max_depth': 3, 'max_features': 2, 'min_sampl...	-0.011064	...	-0.000477	-0.000515	-0.000733	-0.001076	-0.001635	-0.003014	-0.004173	-0.006568	-0.001894	0.001972
2	0.106315	0.010900	0.003392	0.000489	3	2	4	250	{'max_depth': 3, 'max_features': 2, 'min_sampl...	-0.011034	...	-0.000093	-0.000138	-0.000268	-0.000416	-0.000569	-0.001099	-0.001645	-0.002426	-0.000677	0.000763

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_max_depth	param_max_features	param_min_samples_leaf	param_n_estimators	params	split0_test_score	...	split2_train_score	split3_train_score	split4_train_score	split5_train_score	split6_train_score	split7_train_score	split8_train_score	split9_train_score	mean_train_score	std_train_score
0	0.027028	0.004645	0.003990	0.001728	3	2	4	50	{'max_depth': 3, 'max_features': 2, 'min_sampl...	-0.014645	...	-0.001422	-0.001333	-0.001570	-0.002522	-0.004138	-0.008360	-0.012606	-0.017716	-0.005256	0.005480
1	0.053158	0.005951	0.003590	0.000489	3	2	4	100	{'max_depth': 3, 'max_features': 2, 'min_sampl...	-0.014442	...	-0.000582	-0.000663	-0.000868	-0.001410	-0.002275	-0.004778	-0.007014	-0.010757	-0.002930	0.003332
2	0.106315	0.012427	0.003591	0.000662	3	2	4	250	{'max_depth': 3, 'max_features': 2, 'min_sampl...	-0.014455	...	-0.000112	-0.000146	-0.000245	-0.000469	-0.000746	-0.001507	-0.002315	-0.003828	-0.000947	0.001191

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_alpha	param_max_iter	param_tol	params	split0_test_score	...	split2_train_score	split3_train_score	split4_train_score	split5_train_score	split6_train_score	split7_train_score	split8_train_score	split9_train_score	mean_train_score	std_train_score
0	0.101225	0.029870	0.003756	0.004432	1e-05	10000	0.0001	{'alpha': 1e-05, 'l1_ratio': 0.0, 'max_iter': ...	-0.010726	...	-0.003562	-0.003286	-0.003884	-0.006108	-0.010456	-0.017418	-0.023895	-0.031797	-0.010722	0.009718
1	0.088894	0.018066	0.001396	0.001424	1e-05	10000	0.001	{'alpha': 1e-05, 'l1_ratio': 0.0, 'max_iter': ...	-0.010726	...	-0.003562	-0.003286	-0.003884	-0.006108	-0.010456	-0.017418	-0.023895	-0.031797	-0.010722	0.009718
2	0.084987	0.022400	0.004686	0.007158	1e-05	10000	0.01	{'alpha': 1e-05, 'l1_ratio': 0.0, 'max_iter': ...	-0.010726	...	-0.003562	-0.003286	-0.003884	-0.006108	-0.010456	-0.017418	-0.023895	-0.031797	-0.010722	0.009718

	mean_fit_time	std_fit_time	mean_score_time	std_score_time	param_alpha	param_max_iter	param_tol	params	split0_test_score	...	split2_train_score	split3_train_score	split4_train_score	split5_train_score	split6_train_score	split7_train_score	split8_train_score	split9_train_score	mean_train_score	std_train_score
0	0.136534	0.031766	0.002992	2.185142e-07	1e-05	10000	0.0001	{'alpha': 1e-05, 'l1_ratio': 0.0, 'max_iter': ...	-0.013791	...	-0.003027	-0.002863	-0.003289	-0.005275	-0.008989	-0.014612	-0.021377	-0.029597	-0.009384	0.009027
1	0.144114	0.020392	0.003192	3.989941e-04	1e-05	10000	0.001	{'alpha': 1e-05, 'l1_ratio': 0.0, 'max_iter': ...	-0.013791	...	-0.003027	-0.002863	-0.003289	-0.005275	-0.008989	-0.014612	-0.021377	-0.029597	-0.009384	0.009027
2	0.135238	0.031426	0.003591	1.197243e-03	1e-05	10000	0.01	{'alpha': 1e-05, 'l1_ratio': 0.0, 'max_iter': ...	-0.013791	...	-0.003027	-0.002863	-0.003289	-0.005275	-0.008989	-0.014612	-0.021377	-0.029597	-0.009384	0.009027

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
Pliegues
Fold 1	0.010250	0.012253	0.040040	0.021895	0.010777	0.012175	0.015468	0.017608	0.009779	0.011421	0.009565	0.011302
Fold 2	0.003465	0.005035	0.006011	0.008859	0.005831	0.004163	0.006113	0.004916	0.003236	0.005702	0.005437	0.004665
Fold 3	0.002359	0.002950	0.002599	0.004666	0.002282	0.002253	0.007964	0.006088	0.002058	0.001663	0.002220	0.002200
Fold 4	0.005817	0.005213	0.008027	0.008158	0.006893	0.007117	0.012659	0.018517	0.005418	0.005386	0.006962	0.007347
Fold 5	0.019228	0.018612	0.015113	0.017661	0.014720	0.014294	0.018877	0.022049	0.018109	0.019953	0.015303	0.015526
Fold 6	0.097717	0.099564	0.030118	0.027917	0.031447	0.029454	0.031626	0.026445	0.094637	0.099538	0.031492	0.028937
Fold 7	0.054043	0.054168	0.047621	0.046455	0.048174	0.044944	0.050317	0.046399	0.049422	0.054665	0.048253	0.045141
Fold 8	0.117732	0.113723	0.064359	0.061369	0.063967	0.061773	0.066249	0.059950	0.118328	0.118405	0.064465	0.062454
Fold 9	0.124888	0.141807	0.085793	0.091884	0.085716	0.091056	0.089009	0.093796	0.121939	0.142605	0.085260	0.091053
Fold 10	0.060135	0.064386	0.029916	0.038327	0.029938	0.036199	0.029868	0.036866	0.048429	0.046525	0.029645	0.035670

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
Pliegues
Fold 1	0.008462	0.011392	0.032479	0.014992	0.007227	0.007082	0.012623	0.017494	0.007774	0.009879	0.006476	0.005981
Fold 2	0.003400	0.005021	0.004186	0.003734	0.004024	0.002734	0.005366	0.003984	0.003021	0.005115	0.003658	0.003443
Fold 3	0.001714	0.002173	0.001866	0.003266	0.001329	0.001685	0.005990	0.004277	0.001440	0.001243	0.001306	0.001632
Fold 4	0.002247	0.002934	0.003994	0.002763	0.002256	0.002159	0.007597	0.010134	0.001828	0.001652	0.001959	0.002001
Fold 5	0.012167	0.013229	0.011721	0.007150	0.011192	0.012083	0.014557	0.018390	0.012484	0.013561	0.012501	0.012333
Fold 6	0.084448	0.087158	0.016943	0.016096	0.017917	0.016959	0.021310	0.012787	0.082231	0.087985	0.020494	0.016918
Fold 7	0.033824	0.033456	0.026278	0.026127	0.025823	0.027261	0.034723	0.028557	0.022534	0.032382	0.026455	0.025546
Fold 8	0.065309	0.056947	0.036142	0.032730	0.035894	0.033170	0.040139	0.031767	0.074404	0.061051	0.037323	0.032217
Fold 9	0.098532	0.118379	0.052798	0.042889	0.055291	0.043395	0.059637	0.043899	0.099885	0.115925	0.054401	0.042336
Fold 10	0.043867	0.055397	0.020134	0.024806	0.020085	0.023185	0.018272	0.020647	0.030652	0.040988	0.019380	0.022019

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
Pliegues
Fold 1	-29.694342	-42.863114	-467.380863	-139.060110	-32.932006	-42.304050	-68.902018	-89.585501	-26.941164	-37.108063	-25.730847	-36.321997
Fold 2	-12.057997	-26.565429	-38.290436	-84.342924	-35.962679	-17.841906	-39.632676	-25.276891	-10.383378	-34.350915	-31.139927	-22.665800
Fold 3	-2.497690	-4.467829	-3.245419	-12.683312	-2.272921	-2.189557	-38.856047	-22.291884	-1.661564	-0.737697	-2.098041	-2.041702
Fold 4	0.208589	0.364302	-0.507292	-0.556680	-0.111366	-0.184671	-2.748629	-7.020625	0.313296	0.321470	-0.133801	-0.262629
Fold 5	-0.550522	-0.452815	0.042057	-0.308119	0.091258	0.143046	-0.494423	-1.038993	-0.375323	-0.669630	0.017847	-0.010932
Fold 6	-3.250044	-3.412252	0.596246	0.653112	0.559831	0.613866	0.554808	0.688739	-2.986368	-3.409879	0.558570	0.627305
Fold 7	-0.253955	-0.259751	0.026343	0.073442	0.003598	0.132760	-0.086999	0.075686	-0.048672	-0.283003	0.000338	0.125114
Fold 8	-0.479284	-0.380258	0.557943	0.598060	0.563308	0.592751	0.531592	0.616436	-0.494295	-0.496241	0.556486	0.583722
Fold 9	0.035836	-0.243110	0.544993	0.478093	0.545815	0.487454	0.510247	0.456143	0.080832	-0.257130	0.550628	0.487489
Fold 10	-2.836451	-3.398041	0.050516	-0.558438	0.049111	-0.390182	0.053565	-0.441872	-1.488205	-1.296420	0.067631	-0.349833

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
Predicciones
Predicción t	0.22	0.23	0.13	0.13	0.13	0.13	0.13	0.13	0.21	0.21	0.13	0.13
Predicción t+1	0.23	0.23	0.18	0.16	0.16	0.14	0.16	0.14	0.22	0.21	0.16	0.14
Predicción t+2	0.23	0.23	0.21	0.17	0.20	0.16	0.19	0.15	0.22	0.22	0.19	0.15
Predicción t+3	0.23	0.23	0.23	0.19	0.21	0.18	0.20	0.17	0.23	0.22	0.20	0.17
Predicción t+4	0.23	0.23	0.27	0.25	0.23	0.22	0.23	0.21	0.23	0.22	0.22	0.21
Predicción t+5	0.23	0.24	0.30	0.25	0.26	0.23	0.26	0.21	0.23	0.22	0.24	0.22
Predicción t+6	0.24	0.24	0.32	0.30	0.27	0.25	0.27	0.24	0.23	0.22	0.25	0.24
Predicción t+7	0.24	0.24	0.35	0.33	0.28	0.28	0.30	0.27	0.23	0.22	0.26	0.27

	RF_lag3	RF_lag5	LR_lag3	LR_lag5	Lasso_lag3	Lasso_lag5	SVR_lag3	SVR_lag5	GBR_lag3	GBR_lag5	EN_lag3	EN_lag5
Predicciones
Predicción t	0.40	0.37	0.78	0.80	0.79	0.80	0.80	0.80	0.45	0.46	0.79	0.80
Predicción t+1	0.37	0.35	0.61	0.69	0.67	0.75	0.69	0.77	0.39	0.44	0.69	0.77
Predicción t+2	0.36	0.35	0.46	0.65	0.54	0.69	0.58	0.73	0.40	0.44	0.58	0.71
Predicción t+3	0.36	0.35	0.39	0.56	0.49	0.61	0.53	0.67	0.39	0.44	0.53	0.64
Predicción t+4	0.36	0.35	0.13	0.28	0.36	0.44	0.38	0.50	0.39	0.44	0.43	0.48
Predicción t+5	0.35	0.34	-0.03	0.24	0.23	0.38	0.23	0.47	0.38	0.44	0.33	0.42
Predicción t+6	0.34	0.33	-0.17	-0.09	0.16	0.24	0.12	0.32	0.38	0.43	0.27	0.30
Predicción t+7	0.34	0.33	-0.41	-0.28	0.05	0.09	-0.04	0.13	0.37	0.42	0.19	0.16

MODELOS PREDICTIVOS COVID MURCIA¶

Sumando sintomáticos y asintomáticos¶

Añadiendo Movilidad¶

Juntando los datos¶

Visualización de series¶

Creación de predictores¶

Normalización de los predictores¶

Predictor lag3 escalado¶

Predictor lag5 escalado¶

Predicción¶

División del conjunto de datos¶

Random Forest¶

Lag 3¶

Predicción t¶

Predicción 7 días adelante¶

Lag 5¶

Predicción t¶

Predicción 7 días adelante¶

Linear Regression¶

Lag 3¶

Predicción t¶

Predicción 7 días adelante¶

Lag 5¶

Predicción t¶

Predicción 7 días adelante¶

SVR¶

Lag 3¶

Predicción t¶

Predicción 7 días adelante¶

Lag 5¶

Predicción t¶

Predicción 7 días adelante¶

Lasso¶

Lag 3¶

Predicción t¶

Predicción 7 días adelante¶

Lag 5¶

Predicción t¶

Predicción 7 días adelante¶

Gradient Boosting Regressor¶

Lag 3¶

Predicción t¶

Predicción 7 días adelante¶

Lag 5¶

Predicción t¶

Predicción 7 días adelante¶

Elastic Net¶

Lag 3¶

Predicción t¶

Predicción 7 días adelante¶

Lag 5¶

Predicción t¶

Predicción 7 días adelante¶

Tablas de errores¶

Train¶

Random Forest¶

Linear Regression¶

Lasso¶

SVR¶

Gradient Boosting Regressor¶

Elastic Net¶

Creación tablas train¶

RMSE¶

MAE¶

R2¶

Test¶

RMSE¶

MAE¶

R2¶